There is significant recent work in representation learning for domain and task
transfer via embedding models [1]}, [2]}, [3]}, [4]}, which can be trained without any supervision and reused for many downstream tasks like predicting speaker identity [5]}, [6]} and commands [7]}, [8]}. Representation transfer is important to warm start deep learning based dialog systems on new task domains.
Part of our motivation in designing this corpus is providing a realistic test case for representation learning approaches to adapt to speech tasks. See [9]} for a review of availaable dialog datasets. We recorded two-sided phone conversations to simulate customer call center interactions in a financial services domain. The dataset is representative of human to human
goal-oriented dialogs for consumer banking with a narrowly scoped set of intents.
